Intraurban differences in the use of ambulatory health services in a large brazilian city

J Urban Health. 2010 Dec;87(6):994-1006. doi: 10.1007/s11524-010-9499-4.

Abstract

A major goal of health systems is to reduce inequities in access to services, that is, to ensure that health care is provided based on health needs rather than social or economic factors. This study aims to identify the determinants of health services utilization among adults in a large Brazilian city and intraurban disparities in health care use. We combine household survey data with census-derived classification of social vulnerability of each household's census tract. The dependent variable was utilization of physician services in the prior 12 months, and the independent variables included predisposing factors, health needs, enabling factors, and context. Prevalence ratios and 95% confidence intervals were estimated by the Hurdle regression model, which combined Poisson regression analysis of factors associated with any doctor visits (dichotomous variable) and zero-truncated negative binomial regression for the analysis of factors associated with the number of visits among those who had at least one. Results indicate that the use of health services was greater among women and increased with age, and was determined primarily by health needs and whether the individual had a regular doctor, even among those living in areas of the city with the worst socio-environmental indicators. The experience of Belo Horizonte may have implications for other world cities, particularly in the development and use of a comprehensive index to identify populations at risk and in order to guide expansion of primary health care services as a means of enhancing equity in health.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Ambulatory Care Facilities / statistics & numerical data*
  • Brazil
  • Confidence Intervals
  • Female
  • Health Services / statistics & numerical data*
  • Health Status Disparities*
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • Multivariate Analysis
  • Odds Ratio
  • Principal Component Analysis
  • Regression Analysis
  • Socioeconomic Factors
  • Urban Population / statistics & numerical data*